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opencv/modules/calib3d/test/test_undistort.cpp
Ian Maquignaz b05631432b Added declaration, definition and unit test for initInverseRectificationMap() 
Fixed trailing whitespace

Update to initInverseRectificationMap documentation for clarity

Added test case for initInverseRectificationMap()
Updated documentation.

Fixed whitespace error in docs

Small update to test function
Now passes success_error_level

final update to inverseRectification documentation
2021-06-07 16:01:11 -04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
namespace opencv_test { namespace {
class CV_DefaultNewCameraMatrixTest : public cvtest::ArrayTest
{
public:
CV_DefaultNewCameraMatrixTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
private:
cv::Size img_size;
cv::Mat camera_mat;
cv::Mat new_camera_mat;
int matrix_type;
bool center_principal_point;
static const int MAX_X = 2048;
static const int MAX_Y = 2048;
//static const int MAX_VAL = 10000;
};
CV_DefaultNewCameraMatrixTest::CV_DefaultNewCameraMatrixTest()
{
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
matrix_type = 0;
center_principal_point = false;
}
void CV_DefaultNewCameraMatrixTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
matrix_type = types[INPUT][0] = types[OUTPUT][0]= types[REF_OUTPUT][0] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][0] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = cvSize(3,3);
}
int CV_DefaultNewCameraMatrixTest::prepare_test_case(int test_case_idx)
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
RNG& rng = ts->get_rng();
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
center_principal_point = ((cvtest::randInt(rng) % 2)!=0);
// Generating camera_mat matrix
double sz = MAX(img_size.width, img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
double a[9] = {0,0,0,0,0,0,0,0,1};
Mat _a(3,3,CV_64F,a);
a[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
Mat& _a0 = test_mat[INPUT][0];
cvtest::convert(_a, _a0, _a0.type());
camera_mat = _a0;
return code;
}
void CV_DefaultNewCameraMatrixTest::run_func()
{
new_camera_mat = cv::getDefaultNewCameraMatrix(camera_mat,img_size,center_principal_point);
}
void CV_DefaultNewCameraMatrixTest::prepare_to_validation( int /*test_case_idx*/ )
{
const Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_OUTPUT][0];
Mat& test_output = test_mat[OUTPUT][0];
Mat& output = new_camera_mat;
cvtest::convert( output, test_output, test_output.type() );
if (!center_principal_point)
{
cvtest::copy(src, dst);
}
else
{
double a[9] = {0,0,0,0,0,0,0,0,1};
Mat _a(3,3,CV_64F,a);
if (matrix_type == CV_64F)
{
a[0] = src.at<double>(0,0);
a[4] = src.at<double>(1,1);
}
else
{
a[0] = src.at<float>(0,0);
a[4] = src.at<float>(1,1);
}
a[2] = (img_size.width - 1)*0.5;
a[5] = (img_size.height - 1)*0.5;
cvtest::convert( _a, dst, dst.type() );
}
}
//---------
class CV_UndistortPointsTest : public cvtest::ArrayTest
{
public:
CV_UndistortPointsTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
void distortPoints(const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs, const CvMat* matR, const CvMat* matP);
private:
bool useDstMat;
static const int N_POINTS = 10;
static const int MAX_X = 2048;
static const int MAX_Y = 2048;
bool zero_new_cam;
bool zero_distortion;
bool zero_R;
cv::Size img_size;
cv::Mat dst_points_mat;
cv::Mat camera_mat;
cv::Mat R;
cv::Mat P;
cv::Mat distortion_coeffs;
cv::Mat src_points;
std::vector<cv::Point2f> dst_points;
};
CV_UndistortPointsTest::CV_UndistortPointsTest()
{
test_array[INPUT].push_back(NULL); // points matrix
test_array[INPUT].push_back(NULL); // camera matrix
test_array[INPUT].push_back(NULL); // distortion coeffs
test_array[INPUT].push_back(NULL); // R matrix
test_array[INPUT].push_back(NULL); // P matrix
test_array[OUTPUT].push_back(NULL); // distorted dst points
test_array[TEMP].push_back(NULL); // dst points
test_array[REF_OUTPUT].push_back(NULL);
useDstMat = false;
zero_new_cam = zero_distortion = zero_R = false;
}
void CV_UndistortPointsTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
//rng.next();
types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = types[TEMP][0]= CV_32FC2;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][3] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][4] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][0] = sizes[OUTPUT][0] = sizes[REF_OUTPUT][0] = sizes[TEMP][0]= cvtest::randInt(rng)%2 ? cvSize(1,N_POINTS) : cvSize(N_POINTS,1);
sizes[INPUT][1] = sizes[INPUT][3] = cvSize(3,3);
sizes[INPUT][4] = cvtest::randInt(rng)%2 ? cvSize(3,3) : cvSize(4,3);
if (cvtest::randInt(rng)%2)
{
if (cvtest::randInt(rng)%2)
{
sizes[INPUT][2] = cvSize(1,4);
}
else
{
sizes[INPUT][2] = cvSize(1,5);
}
}
else
{
if (cvtest::randInt(rng)%2)
{
sizes[INPUT][2] = cvSize(4,1);
}
else
{
sizes[INPUT][2] = cvSize(5,1);
}
}
}
int CV_UndistortPointsTest::prepare_test_case(int test_case_idx)
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
useDstMat = (cvtest::randInt(rng) % 2) == 0;
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
int dist_size = test_mat[INPUT][2].cols > test_mat[INPUT][2].rows ? test_mat[INPUT][2].cols : test_mat[INPUT][2].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
vector<double> dist(dist_size);
vector<double> proj(test_mat[INPUT][4].cols * test_mat[INPUT][4].rows);
vector<Point2d> points(N_POINTS);
Mat _camera(3,3,CV_64F,cam);
Mat _distort(test_mat[INPUT][2].rows,test_mat[INPUT][2].cols,CV_64F,&dist[0]);
Mat _proj(test_mat[INPUT][4].size(), CV_64F, &proj[0]);
Mat _points(test_mat[INPUT][0].size(), CV_64FC2, &points[0]);
_proj = Scalar::all(0);
//Generating points
for( int i = 0; i < N_POINTS; i++ )
{
points[i].x = cvtest::randReal(rng)*img_size.width;
points[i].y = cvtest::randReal(rng)*img_size.height;
}
//Generating camera matrix
double sz = MAX(img_size.width,img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
cam[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
cam[4] = aspect_ratio*cam[0];
//Generating distortion coeffs
dist[0] = cvtest::randReal(rng)*0.06 - 0.03;
dist[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( dist[0]*dist[1] > 0 )
dist[1] = -dist[1];
if( cvtest::randInt(rng)%4 != 0 )
{
dist[2] = cvtest::randReal(rng)*0.004 - 0.002;
dist[3] = cvtest::randReal(rng)*0.004 - 0.002;
if (dist_size > 4)
dist[4] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
{
dist[2] = dist[3] = 0;
if (dist_size > 4)
dist[4] = 0;
}
//Generating P matrix (projection)
if( test_mat[INPUT][4].cols != 4 )
{
proj[8] = 1;
if (cvtest::randInt(rng)%2 == 0) // use identity new camera matrix
{
proj[0] = 1;
proj[4] = 1;
}
else
{
proj[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
proj[4] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
proj[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
proj[5] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
}
}
else
{
proj[10] = 1;
proj[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
proj[5] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
proj[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
proj[6] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
proj[3] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
proj[7] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
proj[11] = (img_size.height + img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
}
//Generating R matrix
Mat _rot(3,3,CV_64F);
Mat rotation(1,3,CV_64F);
rotation.at<double>(0) = CV_PI*(cvtest::randReal(rng) - (double)0.5); // phi
rotation.at<double>(1) = CV_PI*(cvtest::randReal(rng) - (double)0.5); // ksi
rotation.at<double>(2) = CV_PI*(cvtest::randReal(rng) - (double)0.5); //khi
cvtest::Rodrigues(rotation, _rot);
//copying data
//src_points = &_points;
_points.convertTo(test_mat[INPUT][0], test_mat[INPUT][0].type());
_camera.convertTo(test_mat[INPUT][1], test_mat[INPUT][1].type());
_distort.convertTo(test_mat[INPUT][2], test_mat[INPUT][2].type());
_rot.convertTo(test_mat[INPUT][3], test_mat[INPUT][3].type());
_proj.convertTo(test_mat[INPUT][4], test_mat[INPUT][4].type());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_R = (cvtest::randInt(rng)%2) == 0 ? false : true;
_points.convertTo(src_points, CV_32F);
camera_mat = test_mat[INPUT][1];
distortion_coeffs = test_mat[INPUT][2];
R = test_mat[INPUT][3];
P = test_mat[INPUT][4];
return code;
}
void CV_UndistortPointsTest::prepare_to_validation(int /*test_case_idx*/)
{
int dist_size = test_mat[INPUT][2].cols > test_mat[INPUT][2].rows ? test_mat[INPUT][2].cols : test_mat[INPUT][2].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
double rot[9] = {1,0,0,0,1,0,0,0,1};
double* dist = new double[dist_size ];
double* proj = new double[test_mat[INPUT][4].cols * test_mat[INPUT][4].rows];
double* points = new double[N_POINTS*2];
double* r_points = new double[N_POINTS*2];
//Run reference calculations
CvMat ref_points= cvMat(test_mat[INPUT][0].rows,test_mat[INPUT][0].cols,CV_64FC2,r_points);
CvMat _camera = cvMat(3,3,CV_64F,cam);
CvMat _rot = cvMat(3,3,CV_64F,rot);
CvMat _distort = cvMat(test_mat[INPUT][2].rows,test_mat[INPUT][2].cols,CV_64F,dist);
CvMat _proj = cvMat(test_mat[INPUT][4].rows,test_mat[INPUT][4].cols,CV_64F,proj);
CvMat _points= cvMat(test_mat[TEMP][0].rows,test_mat[TEMP][0].cols,CV_64FC2,points);
Mat __camera = cvarrToMat(&_camera);
Mat __distort = cvarrToMat(&_distort);
Mat __rot = cvarrToMat(&_rot);
Mat __proj = cvarrToMat(&_proj);
Mat __points = cvarrToMat(&_points);
Mat _ref_points = cvarrToMat(&ref_points);
cvtest::convert(test_mat[INPUT][1], __camera, __camera.type());
cvtest::convert(test_mat[INPUT][2], __distort, __distort.type());
cvtest::convert(test_mat[INPUT][3], __rot, __rot.type());
cvtest::convert(test_mat[INPUT][4], __proj, __proj.type());
if (useDstMat)
{
CvMat temp = cvMat(dst_points_mat);
for (int i=0;i<N_POINTS*2;i++)
{
points[i] = temp.data.fl[i];
}
}
else
{
for (int i=0;i<N_POINTS;i++)
{
points[2*i] = dst_points[i].x;
points[2*i+1] = dst_points[i].y;
}
}
CvMat* input2 = zero_distortion ? 0 : &_distort;
CvMat* input3 = zero_R ? 0 : &_rot;
CvMat* input4 = zero_new_cam ? 0 : &_proj;
distortPoints(&_points,&ref_points,&_camera,input2,input3,input4);
Mat& dst = test_mat[REF_OUTPUT][0];
cvtest::convert(_ref_points, dst, dst.type());
cvtest::copy(test_mat[INPUT][0], test_mat[OUTPUT][0]);
delete[] dist;
delete[] proj;
delete[] points;
delete[] r_points;
}
void CV_UndistortPointsTest::run_func()
{
cv::Mat input2,input3,input4;
input2 = zero_distortion ? cv::Mat() : cv::Mat(test_mat[INPUT][2]);
input3 = zero_R ? cv::Mat() : cv::Mat(test_mat[INPUT][3]);
input4 = zero_new_cam ? cv::Mat() : cv::Mat(test_mat[INPUT][4]);
if (useDstMat)
{
//cv::undistortPoints(src_points,dst_points_mat,camera_mat,distortion_coeffs,R,P);
cv::undistortPoints(src_points,dst_points_mat,camera_mat,input2,input3,input4);
}
else
{
//cv::undistortPoints(src_points,dst_points,camera_mat,distortion_coeffs,R,P);
cv::undistortPoints(src_points,dst_points,camera_mat,input2,input3,input4);
}
}
void CV_UndistortPointsTest::distortPoints(const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs,
const CvMat* matR, const CvMat* matP)
{
double a[9];
CvMat* __P;
if ((!matP)||(matP->cols == 3))
__P = cvCreateMat(3,3,CV_64F);
else
__P = cvCreateMat(3,4,CV_64F);
if (matP)
{
cvtest::convert(cvarrToMat(matP), cvarrToMat(__P), -1);
}
else
{
cvZero(__P);
__P->data.db[0] = 1;
__P->data.db[4] = 1;
__P->data.db[8] = 1;
}
CvMat* __R = cvCreateMat(3,3,CV_64F);
if (matR)
{
cvCopy(matR,__R);
}
else
{
cvZero(__R);
__R->data.db[0] = 1;
__R->data.db[4] = 1;
__R->data.db[8] = 1;
}
for (int i=0;i<N_POINTS;i++)
{
int movement = __P->cols > 3 ? 1 : 0;
double x = (_src->data.db[2*i]-__P->data.db[2])/__P->data.db[0];
double y = (_src->data.db[2*i+1]-__P->data.db[5+movement])/__P->data.db[4+movement];
CvMat inverse = cvMat(3,3,CV_64F,a);
cvInvert(__R,&inverse);
double w1 = x*inverse.data.db[6]+y*inverse.data.db[7]+inverse.data.db[8];
double _x = (x*inverse.data.db[0]+y*inverse.data.db[1]+inverse.data.db[2])/w1;
double _y = (x*inverse.data.db[3]+y*inverse.data.db[4]+inverse.data.db[5])/w1;
//Distortions
double __x = _x;
double __y = _y;
if (_distCoeffs)
{
double r2 = _x*_x+_y*_y;
__x = _x*(1+_distCoeffs->data.db[0]*r2+_distCoeffs->data.db[1]*r2*r2)+
2*_distCoeffs->data.db[2]*_x*_y+_distCoeffs->data.db[3]*(r2+2*_x*_x);
__y = _y*(1+_distCoeffs->data.db[0]*r2+_distCoeffs->data.db[1]*r2*r2)+
2*_distCoeffs->data.db[3]*_x*_y+_distCoeffs->data.db[2]*(r2+2*_y*_y);
if ((_distCoeffs->cols > 4) || (_distCoeffs->rows > 4))
{
__x+=_x*_distCoeffs->data.db[4]*r2*r2*r2;
__y+=_y*_distCoeffs->data.db[4]*r2*r2*r2;
}
}
_dst->data.db[2*i] = __x*_cameraMatrix->data.db[0]+_cameraMatrix->data.db[2];
_dst->data.db[2*i+1] = __y*_cameraMatrix->data.db[4]+_cameraMatrix->data.db[5];
}
cvReleaseMat(&__R);
cvReleaseMat(&__P);
}
double CV_UndistortPointsTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 5e-2;
}
//------------------------------------------------------
class CV_InitUndistortRectifyMapTest : public cvtest::ArrayTest
{
public:
CV_InitUndistortRectifyMapTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
private:
static const int MAX_X = 1024;
static const int MAX_Y = 1024;
bool zero_new_cam;
bool zero_distortion;
bool zero_R;
cv::Size img_size;
int map_type;
};
CV_InitUndistortRectifyMapTest::CV_InitUndistortRectifyMapTest()
{
test_array[INPUT].push_back(NULL); // camera matrix
test_array[INPUT].push_back(NULL); // distortion coeffs
test_array[INPUT].push_back(NULL); // R matrix
test_array[INPUT].push_back(NULL); // new camera matrix
test_array[OUTPUT].push_back(NULL); // distorted mapx
test_array[OUTPUT].push_back(NULL); // distorted mapy
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
zero_distortion = zero_new_cam = zero_R = false;
map_type = 0;
}
void CV_InitUndistortRectifyMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
//rng.next();
map_type = CV_32F;
types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = map_type;
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
types[INPUT][0] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][3] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[OUTPUT][0] = sizes[OUTPUT][1] = sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = img_size;
sizes[INPUT][0] = sizes[INPUT][2] = sizes[INPUT][3] = cvSize(3,3);
Size dsize;
if (cvtest::randInt(rng)%2)
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(1,4);
}
else
{
dsize = Size(1,5);
}
}
else
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(4,1);
}
else
{
dsize = Size(5,1);
}
}
sizes[INPUT][1] = dsize;
}
int CV_InitUndistortRectifyMapTest::prepare_test_case(int test_case_idx)
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
int dist_size = test_mat[INPUT][1].cols > test_mat[INPUT][1].rows ? test_mat[INPUT][1].cols : test_mat[INPUT][1].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
vector<double> dist(dist_size);
vector<double> new_cam(test_mat[INPUT][3].cols * test_mat[INPUT][3].rows);
Mat _camera(3,3,CV_64F,cam);
Mat _distort(test_mat[INPUT][1].size(),CV_64F,&dist[0]);
Mat _new_cam(test_mat[INPUT][3].size(),CV_64F,&new_cam[0]);
//Generating camera matrix
double sz = MAX(img_size.width,img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
cam[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
cam[4] = aspect_ratio*cam[0];
//Generating distortion coeffs
dist[0] = cvtest::randReal(rng)*0.06 - 0.03;
dist[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( dist[0]*dist[1] > 0 )
dist[1] = -dist[1];
if( cvtest::randInt(rng)%4 != 0 )
{
dist[2] = cvtest::randReal(rng)*0.004 - 0.002;
dist[3] = cvtest::randReal(rng)*0.004 - 0.002;
if (dist_size > 4)
dist[4] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
{
dist[2] = dist[3] = 0;
if (dist_size > 4)
dist[4] = 0;
}
//Generating new camera matrix
_new_cam = Scalar::all(0);
new_cam[8] = 1;
//new_cam[0] = cam[0];
//new_cam[4] = cam[4];
//new_cam[2] = cam[2];
//new_cam[5] = cam[5];
new_cam[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
new_cam[4] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
new_cam[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
new_cam[5] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
//Generating R matrix
Mat _rot(3,3,CV_64F);
Mat rotation(1,3,CV_64F);
rotation.at<double>(0) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // phi
rotation.at<double>(1) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // ksi
rotation.at<double>(2) = CV_PI/3*(cvtest::randReal(rng) - (double)0.5); //khi
cvtest::Rodrigues(rotation, _rot);
//cvSetIdentity(_rot);
//copying data
cvtest::convert( _camera, test_mat[INPUT][0], test_mat[INPUT][0].type());
cvtest::convert( _distort, test_mat[INPUT][1], test_mat[INPUT][1].type());
cvtest::convert( _rot, test_mat[INPUT][2], test_mat[INPUT][2].type());
cvtest::convert( _new_cam, test_mat[INPUT][3], test_mat[INPUT][3].type());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_R = (cvtest::randInt(rng)%2) == 0 ? false : true;
return code;
}
void CV_InitUndistortRectifyMapTest::prepare_to_validation(int/* test_case_idx*/)
{
cvtest::initUndistortMap(test_mat[INPUT][0],
zero_distortion ? cv::Mat() : test_mat[INPUT][1],
zero_R ? cv::Mat() : test_mat[INPUT][2],
zero_new_cam ? test_mat[INPUT][0] : test_mat[INPUT][3],
img_size, test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][1],
test_mat[REF_OUTPUT][0].type());
}
void CV_InitUndistortRectifyMapTest::run_func()
{
cv::Mat camera_mat = test_mat[INPUT][0];
cv::Mat dist = zero_distortion ? cv::Mat() : test_mat[INPUT][1];
cv::Mat R = zero_R ? cv::Mat() : test_mat[INPUT][2];
cv::Mat new_cam = zero_new_cam ? cv::Mat() : test_mat[INPUT][3];
cv::Mat& mapx = test_mat[OUTPUT][0], &mapy = test_mat[OUTPUT][1];
cv::initUndistortRectifyMap(camera_mat,dist,R,new_cam,img_size,map_type,mapx,mapy);
}
double CV_InitUndistortRectifyMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 8;
}
//------------------------------------------------------
class CV_InitInverseRectificationMapTest : public cvtest::ArrayTest
{
public:
CV_InitInverseRectificationMapTest();
protected:
int prepare_test_case (int test_case_idx);
void prepare_to_validation( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
double get_success_error_level( int test_case_idx, int i, int j );
void run_func();
private:
static const int MAX_X = 1024;
static const int MAX_Y = 1024;
bool zero_new_cam;
bool zero_distortion;
bool zero_R;
cv::Size img_size;
int map_type;
};
CV_InitInverseRectificationMapTest::CV_InitInverseRectificationMapTest()
{
test_array[INPUT].push_back(NULL); // camera matrix
test_array[INPUT].push_back(NULL); // distortion coeffs
test_array[INPUT].push_back(NULL); // R matrix
test_array[INPUT].push_back(NULL); // new camera matrix
test_array[OUTPUT].push_back(NULL); // inverse rectified mapx
test_array[OUTPUT].push_back(NULL); // inverse rectified mapy
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
zero_distortion = zero_new_cam = zero_R = false;
map_type = 0;
}
void CV_InitInverseRectificationMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
cvtest::ArrayTest::get_test_array_types_and_sizes(test_case_idx,sizes,types);
RNG& rng = ts->get_rng();
//rng.next();
map_type = CV_32F;
types[OUTPUT][0] = types[OUTPUT][1] = types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = map_type;
img_size.width = cvtest::randInt(rng) % MAX_X + 1;
img_size.height = cvtest::randInt(rng) % MAX_Y + 1;
types[INPUT][0] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][3] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[OUTPUT][0] = sizes[OUTPUT][1] = sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = img_size;
sizes[INPUT][0] = sizes[INPUT][2] = sizes[INPUT][3] = cvSize(3,3);
Size dsize;
if (cvtest::randInt(rng)%2)
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(1,4);
}
else
{
dsize = Size(1,5);
}
}
else
{
if (cvtest::randInt(rng)%2)
{
dsize = Size(4,1);
}
else
{
dsize = Size(5,1);
}
}
sizes[INPUT][1] = dsize;
}
int CV_InitInverseRectificationMapTest::prepare_test_case(int test_case_idx)
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
if (code <= 0)
return code;
int dist_size = test_mat[INPUT][1].cols > test_mat[INPUT][1].rows ? test_mat[INPUT][1].cols : test_mat[INPUT][1].rows;
double cam[9] = {0,0,0,0,0,0,0,0,1};
vector<double> dist(dist_size);
vector<double> new_cam(test_mat[INPUT][3].cols * test_mat[INPUT][3].rows);
Mat _camera(3,3,CV_64F,cam);
Mat _distort(test_mat[INPUT][1].size(),CV_64F,&dist[0]);
Mat _new_cam(test_mat[INPUT][3].size(),CV_64F,&new_cam[0]);
//Generating camera matrix
double sz = MAX(img_size.width,img_size.height);
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
cam[2] = (img_size.width - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[5] = (img_size.height - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
cam[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
cam[4] = aspect_ratio*cam[0];
//Generating distortion coeffs
dist[0] = cvtest::randReal(rng)*0.06 - 0.03;
dist[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( dist[0]*dist[1] > 0 )
dist[1] = -dist[1];
if( cvtest::randInt(rng)%4 != 0 )
{
dist[2] = cvtest::randReal(rng)*0.004 - 0.002;
dist[3] = cvtest::randReal(rng)*0.004 - 0.002;
if (dist_size > 4)
dist[4] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
{
dist[2] = dist[3] = 0;
if (dist_size > 4)
dist[4] = 0;
}
//Generating new camera matrix
_new_cam = Scalar::all(0);
new_cam[8] = 1;
// If P == K
//new_cam[0] = cam[0];
//new_cam[4] = cam[4];
//new_cam[2] = cam[2];
//new_cam[5] = cam[5];
// If P != K
new_cam[0] = cam[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[0]; //10%
new_cam[4] = cam[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*cam[4]; //10%
new_cam[2] = cam[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.width; //15%
new_cam[5] = cam[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*img_size.height; //15%
//Generating R matrix
Mat _rot(3,3,CV_64F);
Mat rotation(1,3,CV_64F);
rotation.at<double>(0) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // phi
rotation.at<double>(1) = CV_PI/8*(cvtest::randReal(rng) - (double)0.5); // ksi
rotation.at<double>(2) = CV_PI/3*(cvtest::randReal(rng) - (double)0.5); //khi
cvtest::Rodrigues(rotation, _rot);
//cvSetIdentity(_rot);
//copying data
cvtest::convert( _camera, test_mat[INPUT][0], test_mat[INPUT][0].type());
cvtest::convert( _distort, test_mat[INPUT][1], test_mat[INPUT][1].type());
cvtest::convert( _rot, test_mat[INPUT][2], test_mat[INPUT][2].type());
cvtest::convert( _new_cam, test_mat[INPUT][3], test_mat[INPUT][3].type());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
zero_R = (cvtest::randInt(rng)%2) == 0 ? false : true;
return code;
}
void CV_InitInverseRectificationMapTest::prepare_to_validation(int/* test_case_idx*/)
{
cvtest::initInverseRectificationMap(test_mat[INPUT][0],
zero_distortion ? cv::Mat() : test_mat[INPUT][1],
zero_R ? cv::Mat() : test_mat[INPUT][2],
zero_new_cam ? test_mat[INPUT][0] : test_mat[INPUT][3],
img_size, test_mat[REF_OUTPUT][0], test_mat[REF_OUTPUT][1],
test_mat[REF_OUTPUT][0].type());
}
void CV_InitInverseRectificationMapTest::run_func()
{
cv::Mat camera_mat = test_mat[INPUT][0];
cv::Mat dist = zero_distortion ? cv::Mat() : test_mat[INPUT][1];
cv::Mat R = zero_R ? cv::Mat() : test_mat[INPUT][2];
cv::Mat new_cam = zero_new_cam ? cv::Mat() : test_mat[INPUT][3];
cv::Mat& mapx = test_mat[OUTPUT][0], &mapy = test_mat[OUTPUT][1];
cv::initInverseRectificationMap(camera_mat,dist,R,new_cam,img_size,map_type,mapx,mapy);
}
double CV_InitInverseRectificationMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 8;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_DefaultNewCameraMatrix, accuracy) { CV_DefaultNewCameraMatrixTest test; test.safe_run(); }
TEST(Calib3d_UndistortPoints, accuracy) { CV_UndistortPointsTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortRectifyMap, accuracy) { CV_InitUndistortRectifyMapTest test; test.safe_run(); }
TEST(Calib3d_InitInverseRectificationMap, accuracy) { CV_InitInverseRectificationMapTest test; test.safe_run(); }
////////////////////////////// undistort /////////////////////////////////
static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& mapy,
Mat* mask=0, int interpolation=CV_INTER_LINEAR )
{
int x, y, k;
int drows = dst.rows, dcols = dst.cols;
int srows = src.rows, scols = src.cols;
const uchar* sptr0 = src.ptr();
int depth = src.depth(), cn = src.channels();
int elem_size = (int)src.elemSize();
int step = (int)(src.step / CV_ELEM_SIZE(depth));
int delta;
if( interpolation != CV_INTER_CUBIC )
{
delta = 0;
scols -= 1; srows -= 1;
}
else
{
delta = 1;
scols = MAX(scols - 3, 0);
srows = MAX(srows - 3, 0);
}
int scols1 = MAX(scols - 2, 0);
int srows1 = MAX(srows - 2, 0);
if( mask )
*mask = Scalar::all(0);
for( y = 0; y < drows; y++ )
{
uchar* dptr = dst.ptr(y);
const float* mx = mapx.ptr<float>(y);
const float* my = mapy.ptr<float>(y);
uchar* m = mask ? mask->ptr(y) : 0;
for( x = 0; x < dcols; x++, dptr += elem_size )
{
float xs = mx[x];
float ys = my[x];
int ixs = cvFloor(xs);
int iys = cvFloor(ys);
if( (unsigned)(ixs - delta - 1) >= (unsigned)scols1 ||
(unsigned)(iys - delta - 1) >= (unsigned)srows1 )
{
if( m )
m[x] = 1;
if( (unsigned)(ixs - delta) >= (unsigned)scols ||
(unsigned)(iys - delta) >= (unsigned)srows )
continue;
}
xs -= ixs;
ys -= iys;
switch( depth )
{
case CV_8U:
{
const uchar* sptr = sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
dptr[k] = (uchar)cvRound(v00);
}
}
break;
case CV_16U:
{
const ushort* sptr = (const ushort*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((ushort*)dptr)[k] = (ushort)cvRound(v00);
}
}
break;
case CV_32F:
{
const float* sptr = (const float*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((float*)dptr)[k] = (float)v00;
}
}
break;
default:
assert(0);
}
}
}
}
class CV_ImgWarpBaseTest : public cvtest::ArrayTest
{
public:
CV_ImgWarpBaseTest( bool warp_matrix );
protected:
int read_params( const cv::FileStorage& fs );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int interpolation;
int max_interpolation;
double spatial_scale_zoom, spatial_scale_decimate;
};
CV_ImgWarpBaseTest::CV_ImgWarpBaseTest( bool warp_matrix )
{
test_array[INPUT].push_back(NULL);
if( warp_matrix )
test_array[INPUT].push_back(NULL);
test_array[INPUT_OUTPUT].push_back(NULL);
test_array[REF_INPUT_OUTPUT].push_back(NULL);
max_interpolation = 5;
interpolation = 0;
element_wise_relative_error = false;
spatial_scale_zoom = 0.01;
spatial_scale_decimate = 0.005;
}
int CV_ImgWarpBaseTest::read_params( const cv::FileStorage& fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ImgWarpBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( CV_MAT_DEPTH(type) == CV_32F )
{
low = Scalar::all(-10.);
high = Scalar::all(10);
}
}
void CV_ImgWarpBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 3;
int cn = cvtest::randInt(rng) % 3 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
cn += cn == 2;
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn);
if( test_array[INPUT].size() > 1 )
types[INPUT][1] = cvtest::randInt(rng) & 1 ? CV_32FC1 : CV_64FC1;
interpolation = cvtest::randInt(rng) % max_interpolation;
}
void CV_ImgWarpBaseTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT || j != 0 )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
Mat& img = test_mat[INPUT][0];
int i, j, cols = img.cols;
int type = img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
double scale = depth == CV_16U ? 1000. : 255.*0.5;
double space_scale = spatial_scale_decimate;
vector<float> buffer(img.cols*cn);
if( code <= 0 )
return code;
if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
test_mat[INPUT_OUTPUT][0].rows >= img.rows )
space_scale = spatial_scale_zoom;
for( i = 0; i < img.rows; i++ )
{
uchar* ptr = img.ptr(i);
switch( cn )
{
case 1:
for( j = 0; j < cols; j++ )
buffer[j] = (float)((sin((i+1)*space_scale)*sin((j+1)*space_scale)+1.)*scale);
break;
case 2:
for( j = 0; j < cols; j++ )
{
buffer[j*2] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*2+1] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 3:
for( j = 0; j < cols; j++ )
{
buffer[j*3] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*3+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*3+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 4:
for( j = 0; j < cols; j++ )
{
buffer[j*4] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*4+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*4+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
buffer[j*4+3] = (float)((sin((i-j)*space_scale)+1.)*scale);
}
break;
default:
assert(0);
}
/*switch( depth )
{
case CV_8U:
for( j = 0; j < cols*cn; j++ )
ptr[j] = (uchar)cvRound(buffer[j]);
break;
case CV_16U:
for( j = 0; j < cols*cn; j++ )
((ushort*)ptr)[j] = (ushort)cvRound(buffer[j]);
break;
case CV_32F:
for( j = 0; j < cols*cn; j++ )
((float*)ptr)[j] = (float)buffer[j];
break;
default:
assert(0);
}*/
cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
cv::Mat dst(1, cols*cn, depth, ptr);
src.convertTo(dst, dst.type());
}
return code;
}
class CV_UndistortTest : public CV_ImgWarpBaseTest
{
public:
CV_UndistortTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool zero_new_cam;
bool zero_distortion;
};
CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int type = types[INPUT][0];
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][1] = cvSize(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
interpolation = CV_INTER_LINEAR;
}
void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortTest::run_func()
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
double CV_UndistortTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_UndistortTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(src.rows, src.cols);
Mat& _new_cam0 = test_mat[INPUT][3];
Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam);
Mat& _a0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat& _k0 = test_mat[INPUT][2];
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
if( cvtest::randInt(rng)%4 != 0 )
{
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
k[2] = k[3] = 0;
new_cam[0] = a[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[0]; //10%
new_cam[4] = a[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[4]; //10%
new_cam[2] = a[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].rows; //15%
new_cam[5] = a[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].cols; //15%
_a.convertTo(_a0, _a0.depth());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
_k.convertTo(_k0, _k0.depth());
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
_new_cam.convertTo(_new_cam0, _new_cam0.depth());
//Testing C++ code
//useCPlus = ((cvtest::randInt(rng) % 2)!=0);
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
return code;
}
void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ )
{
Mat& output = test_mat[INPUT_OUTPUT][0];
input_output.convertTo(output, output.type());
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][1], test_mat[INPUT][2],
Mat(), Mat(), dst.size(), mapx, mapy, CV_32F );
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask, interpolation );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
class CV_UndistortMapTest : public cvtest::ArrayTest
{
public:
CV_UndistortMapTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool dualChannel;
};
CV_UndistortMapTest::CV_UndistortMapTest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
}
void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
Size sz = sizes[OUTPUT][0];
types[INPUT][0] = types[INPUT][1] = depth;
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F;
sizes[INPUT][0] = cvSize(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
sz.width = MAX(sz.width,16);
sz.height = MAX(sz.height,16);
sizes[OUTPUT][0] = sizes[OUTPUT][1] =
sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = sz;
}
void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortMapTest::run_func()
{
cv::Mat a = test_mat[INPUT][0], k = test_mat[INPUT][1];
cv::Mat &mapx = test_mat[OUTPUT][0], &mapy = !dualChannel ? test_mat[OUTPUT][1] : mapx;
cv::Size mapsz = test_mat[OUTPUT][0].size();
cv::initUndistortRectifyMap(a, k, cv::Mat(), a,
mapsz, dualChannel ? CV_32FC2 : CV_32FC1,
mapx, !dualChannel ? cv::_InputOutputArray(mapy) : cv::noArray());
}
double CV_UndistortMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-3;
}
int CV_UndistortMapTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
const Mat& mapx = test_mat[OUTPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(mapx.rows, mapx.cols);
Mat& _a0 = test_mat[INPUT][0], &_k0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (mapx.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (mapx.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
_a.convertTo(_a0, _a0.depth());
_k.convertTo(_k0, _k0.depth());
if (dualChannel)
{
test_mat[REF_OUTPUT][1] = Scalar::all(0);
test_mat[OUTPUT][1] = Scalar::all(0);
}
return code;
}
void CV_UndistortMapTest::prepare_to_validation( int )
{
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][0], test_mat[INPUT][1], Mat(), Mat(),
test_mat[REF_OUTPUT][0].size(), mapx, mapy, CV_32F );
if( !dualChannel )
{
mapx.copyTo(test_mat[REF_OUTPUT][0]);
mapy.copyTo(test_mat[REF_OUTPUT][1]);
}
else
{
Mat p[2] = {mapx, mapy};
cv::merge(p, 2, test_mat[REF_OUTPUT][0]);
}
}
TEST(Calib3d_UndistortImgproc, accuracy) { CV_UndistortTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortMap, accuracy) { CV_UndistortMapTest test; test.safe_run(); }
TEST(Calib3d_UndistortPoints, inputShape)
{
//https://github.com/opencv/opencv/issues/14423
Matx33d cameraMatrix = Matx33d::eye();
{
//2xN 1-channel
Mat imagePoints(2, 3, CV_32FC1);
imagePoints.at<float>(0,0) = 320; imagePoints.at<float>(1,0) = 240;
imagePoints.at<float>(0,1) = 0; imagePoints.at<float>(1,1) = 240;
imagePoints.at<float>(0,2) = 320; imagePoints.at<float>(1,2) = 0;
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.cols);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<float>(0,i), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<float>(1,i), std::numeric_limits<float>::epsilon());
}
}
{
//Nx2 1-channel
Mat imagePoints(3, 2, CV_32FC1);
imagePoints.at<float>(0,0) = 320; imagePoints.at<float>(0,1) = 240;
imagePoints.at<float>(1,0) = 0; imagePoints.at<float>(1,1) = 240;
imagePoints.at<float>(2,0) = 320; imagePoints.at<float>(2,1) = 0;
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.rows);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<float>(i,0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<float>(i,1), std::numeric_limits<float>::epsilon());
}
}
{
//1xN 2-channel
Mat imagePoints(1, 3, CV_32FC2);
imagePoints.at<Vec2f>(0,0) = Vec2f(320, 240);
imagePoints.at<Vec2f>(0,1) = Vec2f(0, 240);
imagePoints.at<Vec2f>(0,2) = Vec2f(320, 0);
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.cols);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<Vec2f>(0,i)(0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<Vec2f>(0,i)(1), std::numeric_limits<float>::epsilon());
}
}
{
//Nx1 2-channel
Mat imagePoints(3, 1, CV_32FC2);
imagePoints.at<Vec2f>(0,0) = Vec2f(320, 240);
imagePoints.at<Vec2f>(1,0) = Vec2f(0, 240);
imagePoints.at<Vec2f>(2,0) = Vec2f(320, 0);
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(normalized.size()), imagePoints.rows);
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints.at<Vec2f>(i,0)(0), std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints.at<Vec2f>(i,0)(1), std::numeric_limits<float>::epsilon());
}
}
{
//vector<Point2f>
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(normalized.size(), imagePoints.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
//vector<Point2d>
vector<Point2d> imagePoints;
imagePoints.push_back(Point2d(320, 240));
imagePoints.push_back(Point2d(0, 240));
imagePoints.push_back(Point2d(320, 0));
vector<Point2d> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(normalized.size(), imagePoints.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<double>::epsilon());
}
}
}
TEST(Calib3d_UndistortPoints, outputShape)
{
Matx33d cameraMatrix = Matx33d::eye();
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//Mat --> will be Nx1 2-channel
Mat normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.rows);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//Nx1 2-channel
Mat normalized(static_cast<int>(imagePoints.size()), 1, CV_32FC2);
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.rows);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(i,0)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//1xN 2-channel
Mat normalized(1, static_cast<int>(imagePoints.size()), CV_32FC2);
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(static_cast<int>(imagePoints.size()), normalized.cols);
for (int i = 0; i < normalized.rows; i++) {
EXPECT_NEAR(normalized.at<Vec2f>(0,i)(0), imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized.at<Vec2f>(0,i)(1), imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2f> imagePoints;
imagePoints.push_back(Point2f(320, 240));
imagePoints.push_back(Point2f(0, 240));
imagePoints.push_back(Point2f(320, 0));
//vector<Point2f>
vector<Point2f> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(imagePoints.size(), normalized.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<float>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<float>::epsilon());
}
}
{
vector<Point2d> imagePoints;
imagePoints.push_back(Point2d(320, 240));
imagePoints.push_back(Point2d(0, 240));
imagePoints.push_back(Point2d(320, 0));
//vector<Point2d>
vector<Point2d> normalized;
undistortPoints(imagePoints, normalized, cameraMatrix, noArray());
EXPECT_EQ(imagePoints.size(), normalized.size());
for (int i = 0; i < static_cast<int>(normalized.size()); i++) {
EXPECT_NEAR(normalized[i].x, imagePoints[i].x, std::numeric_limits<double>::epsilon());
EXPECT_NEAR(normalized[i].y, imagePoints[i].y, std::numeric_limits<double>::epsilon());
}
}
}
TEST(Imgproc_undistort, regression_15286)
{
double kmat_data[9] = { 3217, 0, 1592, 0, 3217, 1201, 0, 0, 1 };
Mat kmat(3, 3, CV_64F, kmat_data);
double dist_coeff_data[5] = { 0.04, -0.4, -0.01, 0.04, 0.7 };
Mat dist_coeffs(5, 1, CV_64F, dist_coeff_data);
Mat img = Mat::zeros(512, 512, CV_8UC1);
img.at<uchar>(128, 128) = 255;
img.at<uchar>(128, 384) = 255;
img.at<uchar>(384, 384) = 255;
img.at<uchar>(384, 128) = 255;
Mat ref = Mat::zeros(512, 512, CV_8UC1);
ref.at<uchar>(Point(24, 98)) = 78;
ref.at<uchar>(Point(24, 99)) = 114;
ref.at<uchar>(Point(25, 98)) = 36;
ref.at<uchar>(Point(25, 99)) = 60;
ref.at<uchar>(Point(27, 361)) = 6;
ref.at<uchar>(Point(28, 361)) = 188;
ref.at<uchar>(Point(28, 362)) = 49;
ref.at<uchar>(Point(29, 361)) = 44;
ref.at<uchar>(Point(29, 362)) = 16;
ref.at<uchar>(Point(317, 366)) = 134;
ref.at<uchar>(Point(317, 367)) = 78;
ref.at<uchar>(Point(318, 366)) = 40;
ref.at<uchar>(Point(318, 367)) = 29;
ref.at<uchar>(Point(310, 104)) = 106;
ref.at<uchar>(Point(310, 105)) = 30;
ref.at<uchar>(Point(311, 104)) = 112;
ref.at<uchar>(Point(311, 105)) = 38;
Mat img_undist;
undistort(img, img_undist, kmat, dist_coeffs);
ASSERT_EQ(0.0, cvtest::norm(img_undist, ref, cv::NORM_INF));
}
TEST(Calib3d_initUndistortRectifyMap, regression_14467)
{
Size size_w_h(512 + 3, 512);
Matx33f k(
6200, 0, size_w_h.width / 2.0f,
0, 6200, size_w_h.height / 2.0f,
0, 0, 1
);
Mat mesh_uv(size_w_h, CV_32FC2);
for (int i = 0; i < size_w_h.height; i++)
{
for (int j = 0; j < size_w_h.width; j++)
{
mesh_uv.at<Vec2f>(i, j) = Vec2f((float)j, (float)i);
}
}
Matx<double, 1, 14> d(
0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0,
0.09, 0.0
);
Mat mapxy, dst;
initUndistortRectifyMap(k, d, noArray(), k, size_w_h, CV_32FC2, mapxy, noArray());
undistortPoints(mapxy.reshape(2, (int)mapxy.total()), dst, k, d, noArray(), k);
dst = dst.reshape(2, mapxy.rows);
EXPECT_LE(cvtest::norm(dst, mesh_uv, NORM_INF), 1e-3);
}
}} // namespace
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